Anthropic agrees $11.6 billion Akamai cloud deal over seven years

Hedge fund Third Point is leading Nscale’s new financing, which the company announced ahead of its planned stock-market listing. Marina Temkin reported the funding for TechCrunch.

The money is structured as convertible notes: they will become equity shares once the flotation is completed. Of the $3.36 billion total, $2.36 billion is available immediately. Existing investor Nvidia is providing the remaining $1 billion, which Nscale is due to receive in mid-November.

That makes the headline total different from the cash available at once. The financing also sits alongside, rather than replaces, the company’s plans to raise money through its public offering.

A listing alongside campus construction

Nscale has filed its flotation paperwork and plans to list in the United States later in 2026. Reported targets put its New York Stock Exchange valuation at $35 billion and the offering at $3 billion; these are not completed fundraising results.

The company was spun out of Australian cryptocurrency miner Arkon Energy two years ago. Its flotation filing puts accumulated contracts above $103 billion, a measure of contracted business rather than cash already collected.

Nscale is developing large data-centre campuses, including sites in Norway and West Virginia.

The financing gives Nscale access to capital while its campuses are under development. Its staged funding and conversion terms matter to prospective shareholders: the notes become part of the equity when the listing completes.

Daniel Low, Satra Ghosh and colleagues analysed approximately 16,000 de-identified Crisis Text Line conversations to study language associated with suicide risk. Jennifer Michalowski described the work for MIT’s McGovern Institute for Brain Research.

Their study, published in the Journal of Psychopathology and Clinical Science, used the service’s assessments to group conversations into three risk levels. The researchers report that their model predicted risk severity in conversations it had not seen during training.

Those assessments concern risk categories in crisis conversations, not proof that the system can predict who will later attempt suicide.

An assessment users can inspect

The team used AI to draft a vocabulary linked to established risk factors, then manually curated it and had expert clinicians confirm its relevance. The resulting lexicon covers 49 factors.

A simpler machine-learning model uses that vocabulary to estimate risk and flag the language behind its assessment. It can run on a personal computer, reducing computing demands and allowing local processing of sensitive text.

The trade-off is context: a lexicon can miss unfamiliar wording or misread a term’s significance. The researchers stress the need for thorough validation before clinical use and continued human involvement. They are sharing the lexicon and software used to build it, allowing researchers to develop vocabularies for other mental-health conditions.

Researchers gain a reusable way to examine mental-health language without relying solely on a large model. For potential clinical users, visible risk factors offer something to inspect, rather than an unexplained score.